Computer Vision Applications

1. COMPUTER
VISION
APPLICATIONS
Table of contents:
02 What is computer
vision?
04 What tools are used
to create computer
vision solutions?
07 Real-life examples
of computer vision
applications
09 Addepto case study
10 About Addepto
ADDEPTO WHITE PAPER 2020

2. Addepto White Paper 2020
Computer vision 02
WHAT IS COMPUTER
VISION?
WHAT IS COMPUTER
VISION?
Computer vision (CV) is an artificial intelligence-based technology that
allows computers to observe the world. By analyzing visual data, this
innovation can almost perfectly understand a particular situation, and
without missing any factors, find the best solutions or the most reasonable
decisions.
The algorithms which stand for computer vision reached an amazing level of
accuracy in understanding what is happening around. Today’s systems are
right in 99% of cases – which makes them more accurate than humans.
Computer Vision applications will become crucial in future automation,
visual intensive works like RTG luggage inspection, finding criminals with
public cameras, or preventing financial fraud using face recognition. This
domain will open new areas of development and help to create new
industries.
OBJECT DETECTIONOBJECT DETECTION
Object Detection is a part of Computer Vision which focuses on detecting
various objects on photos like cats, dogs, cars, bikes, humans, etc., by
extracting features from pixels and applying deep learning to recognize
patterns. One of the main areas of Object Detection is face recognition.
Algorithms of Computer Vision are able to reconstruct 3D objects from 2D
imagery taken from different angles. As an example, we can acquire a city
model from images gathered by drones. We may even create a model of the
cave based on a movie recorded inside it.
3D SCENE RECONSTRUCTION3D SCENE RECONSTRUCTION

3. A model trained to detect objects on
photos can extract its content and
prepare tags automatically.
Nowadays, the inference is so fast
that videos can be processed in real-
time. This technology can be used in
personalized advertisements (for
example screens in public space)
where ads are chosen basing on
your clothes and things you carry.
Traditionally, to detect an object on
an image it used to be sufficient to
just select its position by the
rectangle. Now, an improvement of
this technique is outlining the given
object (for example by a slight
change of its color) and in that way
segment image on different objects
where the result is obtaining an
image very similar to the stained
glass. This technology will be
extensively used in autonomous
navigation and radiology (outlining
cancerous changes in tissue).
Addepto White Paper 2020
Computer vision
IMAGE AND VIDEO PRE-
PROCESSING
IMAGE AND VIDEO PRE-
PROCESSING
Advanced CV with the use of neural
networks can perform image
transformations not available for
traditional image processing
algorithms. As an example, we can
artificially increase the number of
trees or remove them without
noticing an artificial change.
It is possible to generate missing
parts of the photo or change the
sky’s appearance from Earth to
Mars. Possibilities of image
enhancing and transformation are
limitless and require just creating a
specialized model for a given task.
VIDEO AND
IMAGE
CONTENT
INDEXING
VIDEO AND
IMAGE
CONTENT
INDEXING
SCENE
SEGMENTATION
SCENE
SEGMENTATION
03

4. WHAT TOOLS ARE USED TO
CREATE COMPUTER VISION
SOLUTIONS?
WHAT TOOLS ARE USED TO
CREATE COMPUTER VISION
SOLUTIONS?
C++ is a programming language which supports procedural, object-oriented, and
generic programming. It is statically typed, compiled, general-purpose, case-
sensitive, free-form framework. It comprises a combination of both high-level
and low-level language features.
Python is one of the most popular programming languages in the world. Is being
used by companies like Wikipedia, Google, Yahoo!, CERN, and NASA.
It’s often used as a “scripting language” for web applications - it can automate a
specific series of tasks, making it more efficient. Python is often used in software
applications, web pages, and games. It is also used in scientific and mathematical
computing, and in AI projects.
OpenCV library is an open-source computer vision and machine learning
software library. It was built primarily to provide an infrastructure for computer
vision applications.
OpenCV library has over 2,500 optimized algorithms, which include either the
computer vision and machine learning algorithms. These algorithms can be used
by companies detect and recognize faces (face recognition), identify objects,
classify human actions in videos, track camera movements, track moving
objects, extract 3D models of objects, find similar images from an image
database, follow eye movements, recognize scenery, and establish markers to
overlay it with augmented reality.
Addepto White Paper 2020
Computer vision
04
C++C++
PYTHONPYTHON
OPEN CVOPEN CV

5. Torch offers a wide support for machine learning algorithms that puts GPUs
(graphics processing units) first. It is very efficient if it comes to fast scripting
language, LuaJIT, and an underlying C/CUDA implementation.
PyCharm is one of many IDEs (integrated development environment) available
for Python. It is user-friendly, powerful, and provides integration with git.
PyCharm has its own terminal, python console, and provides support for various
useful plugins.
Keras is a high-level library that uses TensorFlow, CNTK, or Theano as a back-
end. It is officially supported by Google (TensorFlow) which has intercepted its
development. Keras positions itself as a CV API for “human beings”. It focuses
on simplicity so creating networks is fast and intuitive.
Model architecture is divided on fully-configurable modules like neural layers,
optimizers (Adam, RMSProp), cost functions, etc. It includes built-in models like
ResNet50, InceptionV3, or MobileNet. Keras can be used on multi-GPU systems
but it requires more time to configure with using both Keras and Tensorflow
API.
Addepto White Paper 2020
Computer vision
05
TORCHTORCH
PYCHARMPYCHARM
KERASKERAS
THEANOTHEANO
Theano is one of the oldest Python libraries built for operating on multi-
dimensional arrays and that allow training neural networks. It is integrated with
NumPy, it has efficient symbolic differentiation, possibilities to evaluate
expressions faster thanks to dynamic C code generation, and can automatically
diagnose many types of errors. Its development has finished in late 2017 but it is
still a decent library to use for your project.
COMPUTER VISION TOOLSCOMPUTER VISION TOOLS

6. TensorFlow was designed by Google Brain Team and released as an open-source
library for abstract (using tensors) numerical computation. It is a low-level
library, old enough to have many sophisticated projects using it as a backbone,
decent documentation, and vast community. TensorFlow’s main advantage (over
Theano) is multi-GPU support. It has two API: low-level (original), and high-level
Keras.
MXNet allows using many GPUs in distributed systems. It is also easy to
manage where every piece of data should be stored in the systems. This library
has also built-in methods for fast derivative calculations. Every coded layer has
been optimized and now MXNet is one of the fastest available CV libraries.
However, it takes it more time to start modeling comparing to Keras.
LASAGNELASAGNE
Lasagne is built on top of Theano with the intention to be simple to understand,
use, and easy to directly process and return Theano expression or NumPy data
types. Lasagne allows defining Convolutional Neural Networks, Recurrent Neural
Networks, and its combinations. It supports CPU and GPU thanks to Theano’s
compiler. In terms of library level, it is medium – somewhere between low-level
libraries like TensorFlow or Theano and high-level libraries like Keras.
Addepto White Paper 2020
Computer vision
06
TENSORFLOWTENSORFLOW
COMPUTER VISION TOOLSCOMPUTER VISION TOOLS
MXNETMXNET

7. Addepto White Paper 2020
Computer vision
07
REAL-LIFE EXAMPLES OF
COMPUTER VISION
APPLICATIONS
REAL-LIFE EXAMPLES OF
COMPUTER VISION
APPLICATIONS
Automatic product detection allows recognizing
missing and misplaced products on shelves with
comparison to the planogram. Aggregated
information about shop conditions gives the
opportunity to improve the quality of customer
service.
RETAIL SHELF ANALYSISRETAIL SHELF ANALYSIS
Computer Vision can also automate the process
of discovering illicit items in luggage during
customs inspection on the airports. Such a
mundane task is ideal for Convolutional Neural
Networks taking into consideration the huge
size of the available data-set.
RTG ANALYSISRTG ANALYSIS
This technology will improve the
advertisement industry, making it more
personalized. For example, after tagging
customer’s favorite brands and gaining
deep insights into their preferences, we can
recommend products with a higher
probability of being chosen. It is a win-win
situation for both customers (more relevant
ads) and e-commerce (higher income).
AUTOMATIC VIDEO
TAGGING FOR REAL-TIME
MARKETING
AUTOMATIC VIDEO
TAGGING FOR REAL-TIME
MARKETING

8. 04
Addepto White Paper 2020
Computer vision
COMPUTER VISION APPLICATIONSCOMPUTER VISION APPLICATIONS
Having real estate imagery data with its value,
we can create a model that will predict value
from new real estate photos. It allows fast
comparison of given and predicted prices in
order to find investment gems or to find
undervalued rent occasions.
REAL ESTATE VALUATIONREAL ESTATE VALUATION
Make identification easier for security officers
and ordinary people – no more need for
additional cards or keys. Also, there is a
possibility to determine when somebody is a
wanted criminal.
RECOGNIZING FACES IN
SECURITY SYSTEMS
RECOGNIZING FACES IN
SECURITY SYSTEMS
This technique protects from misspelling and it
is much faster than reading information
manually. It has the potential to simplify
maintaining a customer database and improve
the quality of data.
AUTOMATIC READING OF
PERSONAL INFORMATION
FROM IDENTITY CARDS
AUTOMATIC READING OF
PERSONAL INFORMATION
FROM IDENTITY CARDS
CV techniques use data from cameras to
visually check the condition of assets, for
example, valves and pipes, and compare it with
optimal conditions. This information can be
transferred to a remote maintenance crew, that
checks anomalies.
INDUSTRIAL MAINTENANCEINDUSTRIAL MAINTENANCE
08

9. Addepto White Paper 2020
Computer vision
09
ADDEPTO CASE STUDYADDEPTO CASE STUDY
There are cases of lost passengers' luggage. Our goal was to find lost
luggage at other airports. The problem is that manual search is a very labor
intensive task because you have to analyze terabytes of videos.
CHALLENGE:CHALLENGE:
To solve the existing problem, we created the Deep Learning model to find
lost luggage. We used FgSegNet for background segmentation, and we used
Triplet and Siam networks to find luggage (accuracy in the top five is 94%).
The final solution was built in C ++ with an intuitive interface for users. The
solutions work in real time using Jetson graphics processors.
OUR SOLUTION:OUR SOLUTION:
The prepared solutions processed terabytes of films within a few hours and
are looking for lost luggage with great accuracy. It saves many hours of
work and optimizes airport costs.
BENEFITS:BENEFITS:
Discover other Addepto case studies.
BAGGAGE SIMILARITYBAGGAGE SIMILARITY

10. contact@addepto.com
Our team builds innovative applications and products by integrating
computer vision services with other systems like POS, ERP, and diagnostic
software. It is used to detect anomalies in shopping centers, track quality in
production lines, analyze medical images, identify products on shelves, and
analyze people and their demographics in social media.
AI development experts at Addepto have outstanding experience in building
customized computer vision applications with advanced components based
on neural networks such as object classification, feature recognition, image
segmentation, pattern recognition, object detection, background
segmentation, and emotion detection. Those solutions help to solve complex
business challenges in different industries.
Addepto White Paper 2020
Computer vision
10
ABOUT ADDEPTOABOUT ADDEPTO
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